class_docstr: Class for Tree search based optimisation. 
code: def __init__(
      self,
      doo_obj,
      nu_max,
      rho_max,
      total_budget,
      K,
      C_init,
      tol=1e-3,
      Randomize=False,
  ):
      self.doo_obj = doo_obj
      self.nu_max = nu_max
      self.rho_max = rho_max
      self.total_budget = total_budget
      self.K = K
      self.C = C_init
      self.tol = tol
      self.value_dict = {}
      self.qcount = 0
      self.last_z_hist = []
      self.Randomize = Randomize
      self.query_vals = []
      self.query_pts = []
  
imports: from __future__ import division
  from functools import total_ordering
  from argparse import Namespace
  import numpy
  from .general_utils import map_to_bounds
label_desc: Use image node_id 1 to create a new OptTree object from inherited base classes: object with example: obj = OptTree(doo_obj, nu_max, rho_max, total_budget, K, C_init, tol, Randomize)